Skip to main content
SpringerLink
Log in

QuanPath: achieving one-step communication for distributed quantum circuit simulation

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

Quantum circuit simulation is an important tool for evaluating designed quantum algorithms. Full-state simulation gives the entire state vectors produced by the running of algorithms. Distributed simulation aims to take advantage of resources on multiple machines (a.k.a. nodes) for high-performance simulation. As a quantum circuit may have many levels, simulation on each level is called a step. The reduction in the cost on each step results in a significant saving in total cost. In existing distributed full-state simulations, the communication cost in each step dominates. In this paper, we propose a new simulation technique, namely QuanPath, which completely eliminates communications and synchronizations on each step until the final merge step. Each node can compute its portion of the state vector independently in parallel. We present detailed mathematical analyses to guarantee the correctness of QuanPath. In the final merge step, an efficient communication scheme is further designed. Experimental results show that when simulating quantum algorithms, QuanPath achieves thousands times of reduction in communication cost and obtains dozens times of simulation acceleration compared with existing techniques. In addition, QuanPath realizes almost linear speedup, so it presents good scalability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Algorithm 1
Fig. 8
Fig. 9
Algorithm 2
Fig. 10
Fig. 11
Algorithm 3
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article.

References

  1. Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41(2), 303–332 (1999)

    Article  ADS  MathSciNet  Google Scholar 

  2. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)

  3. Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S.C., Endo, S., Fujii, K., McClean, J.R., Mitarai, K., Yuan, X., Cincio, L.: Variational quantum algorithms. Nat. Rev. Phys. 3(9), 625–644 (2021)

    Article  Google Scholar 

  4. Zhang, K., Liu, L., Hsieh, M.-H., Tao, D.: Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits. Adv. Neural. Inf. Process. Syst. 35, 18612–18627 (2022)

    Google Scholar 

  5. Hu, Z., Lin, Y., Guan, Q., Jiang, W.: Battle against fluctuating quantum noise: Compression-aided framework to enable robust quantum neural network. arXiv preprint arXiv:2304.04666 (2023)

  6. Wang, H., Ding, Y., Gu, J., Lin, Y., Pan, D.Z., Chong, F.T., Han, S.: Quantumnas: Noise-adaptive search for robust quantum circuits. In: 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 692–708 (2022). IEEE

  7. List of QC simulators. https://www.quantiki.org/wiki/list-qc-simulators (2023)

  8. Burgholzer, L., Ploier, A., Wille, R.: Exploiting arbitrary paths for the simulation of quantum circuits with decision diagrams. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 64–67 (2022). IEEE

  9. Boixo, S., Isakov, S.V., Smelyanskiy, V.N., Neven, H.: Simulation of low-depth quantum circuits as complex undirected graphical models. arXiv preprint arXiv:1712.05384 (2017)

  10. Wu, X.-C., Di, S., Dasgupta, E.M., Cappello, F., Finkel, H., Alexeev, Y., Chong, F.T.: Full-state quantum circuit simulation by using data compression. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–24 (2019)

  11. Steiger, D.S., Häner, T., Troyer, M.: ProjectQ: an open source software framework for quantum computing. Quantum 2, 49 (2018)

    Article  Google Scholar 

  12. Smelyanskiy, M., Sawaya, N.P., Aspuru-Guzik, A.: qHiPSTER: the quantum high performance software testing environment. arXiv preprint arXiv:1601.07195 (2016)

  13. De Raedt, K., Michielsen, K., De Raedt, H., Trieu, B., Arnold, G., Richter, M., Lippert, T., Watanabe, H., Ito, N.: Massively parallel quantum computer simulator. Comput. Phys. Commun. 176(2), 121–136 (2007)

    Article  ADS  Google Scholar 

  14. De Raedt, H., Jin, F., Willsch, D., Willsch, M., Yoshioka, N., Ito, N., Yuan, S., Michielsen, K.: Massively parallel quantum computer simulator, eleven years later. Comput. Phys. Commun. 237, 47–61 (2019)

    Article  ADS  Google Scholar 

  15. Häner, T., Steiger, D.S.: 0.5 petabyte simulation of a 45-qubit quantum circuit. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–10 (2017)

  16. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  17. Jones, T., Brown, A., Bush, I., Benjamin, S.C.: Quest and high performance simulation of quantum computers. Sci. Rep. 9(1), 10736 (2019)

    Article  ADS  Google Scholar 

  18. Pednault, E., Gunnels, J.A., Nannicini, G., Horesh, L., Magerlein, T., Solomonik, E., Wisnieff, R.: Breaking the 49-qubit barrier in the simulation of quantum circuits. arXiv preprint arXiv:1710.0586715 (2017)

  19. McCaskey, A., Dumitrescu, E., Chen, M., Lyakh, D., Humble, T.: Validating quantum-classical programming models with tensor network simulations. PLoS ONE 13(12), 0206704 (2018)

    Article  Google Scholar 

  20. Ahmadzadeh, A., Sarbazi-Azad, H.: Fast and scalable quantum computing simulation on multi-core and many-core platforms. Quantum Inf. Process. 22(5), 215 (2023)

    Article  ADS  MathSciNet  Google Scholar 

  21. Fatima, A., Markov, I.L.: Faster schrödinger-style simulation of quantum circuits. In: 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 194–207 (2021). IEEE

  22. Huang, Y., Martonosi, M.: Statistical assertions for validating patterns and finding bugs in quantum programs. In: Proceedings of the 46th International Symposium on Computer Architecture (ISCA), pp. 541–553 (2019)

  23. Li, R., Wu, B., Ying, M., Sun, X., Yang, G.: Quantum supremacy circuit simulation on sunway taihulight. IEEE Trans. Parallel Distrib. Syst. 31(4), 805–816 (2020)

    Article  Google Scholar 

  24. Chen, J., Zhang, F., Huang, C., Newman, M., Shi, Y.: Classical simulation of intermediate-size quantum circuits. arXiv preprint arXiv:1805.01450 (2018)

  25. Boixo, S., Isakov, S.V., Smelyanskiy, V.N., Babbush, R., Ding, N., Jiang, Z., Bremner, M.J., Martinis, J.M., Neven, H.: Characterizing quantum supremacy in near-term devices. Nat. Phys. 14(6), 595–600 (2018)

    Article  Google Scholar 

  26. Zhou, Y., Stoudenmire, E.M., Waintal, X.: What limits the simulation of quantum computers? Phys. Rev. X 10(4), 041038 (2020)

    Google Scholar 

  27. Holmes, Z., Sharma, K., Cerezo, M., Coles, P.J.: Connecting ansatz expressibility to gradient magnitudes and barren plateaus. PRX Quantum 3(1), 010313 (2022)

    Article  ADS  Google Scholar 

  28. Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quantum Technol. 2(12), 1900070 (2019)

    Article  Google Scholar 

Download references

Funding

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 62372182, 62372183).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, East China Normal University, Putuo District, Shanghai, 200062, China

    Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Wenlong Xiao, Qijun Dai & Longshan Xu

Authors
  1. Yuhong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edwin Hsing-Mean Sha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingfeng Zhuge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenlong Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qijun Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Longshan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingfeng Zhuge.

Ethics declarations

Conflict of interest

The authors have declared that they do not have any conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Y., Sha, E.HM., Zhuge, Q. et al. QuanPath: achieving one-step communication for distributed quantum circuit simulation. Quantum Inf Process 23, 1 (2024). https://doi.org/10.1007/s11128-023-04192-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-023-04192-x

Keywords

Search

Navigation